AA->HH Study ~About the Neural Network analysis and other issues~

16th ILC general meeting (2010/7/17)

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AA->HH Study~About the Neural Network analysis and other issues~Shin-ichi Kawada

(Advanced Science of Matter, Hiroshima University)


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About PLCElectrons

Backward Compton scattering

Photon-photon collision


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Reaction in photon-photon collision Signal: gamma ・ gamma->HH

Final goal: measurement of higgs self-coupling constant

Many backgrounds gamma ・ gamma->WW, ZZ, 4b, etc...

Analysis of background reduction is necessary. It was assumed that higgs mass is 120GeV in this

study.


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Main backgrounds

Optimized CM energy: 270GeV

There are 2 main backgrounds.

gamma・ gamma->WW (11.6pb)gamma・ gamma->ZZ (9.42fb)

(studied by N.Maeda)


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Previous results (studied by N.Maeda) The neural network (NN) analysis can reduce

WW backgrounds. However, ZZ backgrounds remain because of

inaccuracy from the jet clustering and the b-tagging.

We will write the paper about the analysis of the background reduction using NN.


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Current issues

Toward the preparation of the paper, there are 3 issues to be solved. Consideration of other backgrounds Treatment of the ZZ events Check of the NN training


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1. Other backgrounds

We have to consider other intermediate states in gamma ・ gamma->4b. We have asked Kurihara-san to calculate the cros

s-section of gamma ・ gamma->4b.


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2. Treatment of ZZ events

γγ ZZ

Present method

1st step 2nd stepZZ 4b

ON-shell mode is helicity state.

More realistic method

γγ intermediate state including ZZ 4b

OFF-shell mode is not helicity state.

reaction


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2. Treatment of ZZ events

We have to compare the helicity amplitude using ON-shell mode with OFF-shell mode.


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Results

CM energyCos(theta)

Phi

Helicity

ON-shell mode

OFF-shell mode


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Results

ON-shell mode results and OFF-shell mode results are almost same.

ON-shell mode calculation is effective. “Treatment of ZZ events” issue is solved!


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3. Check of the NN training

We have to make an estimation of the systematic error from NN analysis.


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3. Check of the NN trainingNN analysis (conventional)

NN weight file No.1signal file No.1

BG file No.1

Training step

Analysis step

some events

some events

weight file No.1

signal file No.1

BG file No.1

ALL events

ALL eventsNN

output

Weight file No.1 depends on the signal file No.1 and BG file No.1.


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3. Check of the NN trainingWe have to check of statistical independence.

NN weight file No.2signal file No.2

BG file No.2

Training step

Analysis step

some events

some events

weight file No.2 (1)

signal file No.1 (2)

BG file No.1 (2)

ALL events

ALL eventsNN output

No.2 files are independent from No.1 files.


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Events file & weight file

There are 4 MC events files & 4 weight files. Maeda MC events file & weight file Kawada MC ON-shell events file & weight file Kawada MC OFF-shell events file No.1 & weight

file Kawada MC OFF-shell events file No.2 & weight

file Events files: HH 50k events (signal)

ZZ 1M events (BG)


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Pre-selection condition

99.0cos

05.0

# of b-flavor jets (nsig=3.0 & noffv=1) > 2

# of b-flavor jets (nsig=3.0 & noffv=2) > 1

These condition come from gamgamZZ-code.

b-tagging

Condition of reconstructed particles


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Pre-selection# of generate

# of remained events after pre-selection

Maeda

Kawada-ON

Kawada-OFF1

Kawada-OFF2

HH=50000

ZZ=1000000

HH=50000

ZZ=1000000

HH=50000

ZZ=1000000

HH=50000

ZZ=1000000

HH=29897

ZZ=86538

HH=29958

ZZ=87056

HH=29958

ZZ=87486

HH=29823

ZZ=87143

These difference is caused by statistical fluctuation.


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How to check statistical independence

NNweight file

Kawada OFF1

Kawada OFF1 signal file

29958

Kawada OFF1 BG file 87056

3000 (fixed)

5000 (fixed)

Example

(4 events file) * (4 weight file) = (16 combinations)

2. We calculate the maximum significance for each combination.

1. Preparation of weight files

3. We compare the values of significance to check the statistical independence among the combination.


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Combination results

MaedaKawada

OFF1

Kawada

OFF2

Kawada

ON

weights

events

Maeda29897 86538

Kawada-OFF129958 87056

Kawada-ON29958 87486

Kawada-OFF229823 87143

14086 43721.34

16051 66621.25

15520 57831.29

15078 55831.28

11704 21971.53

13575 30571.52

13168 28961.52

13738 30571.54

12501 26031.51

13880 32931.51

14524 34881.50

13738 31441.52

12167 23891.53

12538 25751.53

12830 27231.52

13291 28461.54

# of remained HH, # of remained ZZ, Maximum significance


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Result & Discussion

Significance ~1.3 was obtained with Maeda events and there is large fluctuation.

Significance ~1.52 was obtained with Kawada events and there is small fluctuation. These fluctuation is caused by the statistical fluctuation among Kawada events.


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Result & Discussion

In Kawada events, maximum significance does not depend on the weight file.

It is still unclear what causes large fluctuation among the Maeda events.


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Summary

The status of 3 issues are presented as following. Consideration of other backgrounds -> Kurihara-s

an Treatment of ZZ events -> ON-shell mode calculat

ion is effective. Check of the NN training -> The fluctuation of sign

ificance is caused by the statistical fluctuation among Kawada events.


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Talk in JPS meeting

I will talk about AA->HH study topics in JPS. English title: Feasibility study of the measurement

of higgs pair creation in the photon-photon linear collider


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Backup slides


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ON-shell mode program

ZZSpring.cxx

HELVector z1im(fP[0]+fP[1], kM_z, -1, -1);

HELVector z1i0(fP[0]+fP[1], kM_z, 0, -1);

HELVector z1ip(fP[0]+fP[1], kM_z, -1, -1);

This “kM_z” means ON-shell mode (91.19GeV).


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OFF-shell mode programZZSpring.cxx

Double_t forinvE1 = TMath::Power((fP[0].E() + fP[1].E()),2);

Double_t forinvPx1 = TMath::Power((fP[0].Px() + fP[1].Px()),2);

Double_t forinvPy1 = TMath::Power((fP[0].Py() + fP[1].Py()),2);

Double_t forinvPz1 = TMath::Power((fP[0].Pz() + fP[1].Pz()),2);

Double_t invmassZ1 = TMath::Sqrt(forinvE1 – forinvPx1 – forinvPy1 – forinvPz1);

HELVector z1im(fP[0]+fP[1], invmassZ1, -1, -1);HELVector z1i0(fP[0]+fP[1], invmassZ1, 0, -1);HELVector z1ip(fP[0]+fP[1], invmassZ1, -1, -1);

This means invariant mass.

These parts show the calculation of invariant mass.

22 pEmZ

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AA->HH Study ~About the Neural Network analysis and other issues~

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